Machine Learning: Bootcamp
Duration: 48 Hours
• Option 1 (Regular): 3 Days/Week - 3 Hours/Session - 16 Sessions
• Option 2 (Intensive): 3 Days/Week - 6 Hours/Session - 8 Sessions
Method: 1 on 1 (onsite)
Schedule is tentative; finalized after enrollment according to student/instructor availability.
Machine Learning has become an integral part of many commercial applications and research projects. This course will teach you practical ways to build your own machine learning solutions. Machine learning is a form of data analysis that gives computers the ability to learn and process information with little human intervention. Because it can be used in numerous fields, it is a promising new technology with tens of thousands of current job openings.
Machine learning is about extracting knowledge from data. It is a research field at the intersection of statistics, artificial intelligence, and computer science and is also known as predictive analytics or statistical learning. The application of machine learning methods has in recent years become ubiquitous in everyday life. From automatic recommendations of which movies to watch, to what food to order or which products to buy, to personalized online radio and recognizing your friends in your photos, many modern websites and devices have machine learning algorithms at their core. When you look at a complex website like Facebook, Amazon, or Netflix, it is very likely that every part of the site contains multiple machine learning models.
This course will cover the following topics;
1. Introduction to Machine Learning. Tools and Infrastructure. Visualization of Results.
2. Introduction to Supervised Learning. Data Preprocessing and Feature Engineering. Imputation of
Missing Values and Feature Selection.
3. Linear Models for Regression.
4. Linear Models for Classification. CART & Random Forests. Ensemble Methods, Gradient
Boosting and Calibration.
5. Support Vector Machines, Model Evaluation.
6. Clustering, Cluster Evaluation.
7. Working with Imbalanced Data. Dimensionality Reduction.
8. Working with Text Data and Natural Language Processing.
9. Neural Networks. Convolutional Neural Networks for Image Recognition. Advanced Neural Networks.
10. Reinforcement Learning. Time Series Data.
• Exploratory Data Analysis using Python libraries.
• Predicting House/Apartment Prices using Regression techniques.
• Visualize the coefficients for logistic regression and linear support vector machines, and feature
importance for decision tree.
• Clustering on Healthcare Data
• Natural Language Processing on online shopping reviews data.
• Pattern Recognition and image processing on neural networks.
What to Expect:
After taking this course, students should know the methods used in machine learning and have hands-on
experience in solving various business problems using R and Python. In this course, students will know
the methods and tools widely applied to the field of machine learning: linear models for regression and
classification, clustering methods, working with text data, neural networks, reinforcement learning, and
other advanced topics. Students will use different business data sets with R and Python.
Pooja Umathe is an Aspiring Data Scientist with strong Analytical background and 3+ years of experience using predictive modeling, data processing, machine learning, and data mining algorithms to solve challenging business problems. She has been involved with Python opensource community and passionate about deep reinforcement learning. She intellectually curious, with strong leadership qualities and communication skills, she also demonstrates strong problem-solving skills, and comfortable in manipulating, wrangling and analyzing large and complex data sets. She comprises hands-on design and implementation skills of machine learning solutions and techniques, time series analysis and statistical methods. She is well versed with analytical tools (SQL, Tableau, Python, R ,Excel) and an interest in more advanced topics in analytics (artificial intelligence, data engineering, deep learning, clustering, decision tree analysis). She has strong analytical skills with excellent track records of her ability to solve unstructured problems involving large amounts of quantitative data with a structured and hypothesis-driven approach.